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Springer Data-Driven Remaining Useful Life Prognosis Book

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Description

Master the complexities of stochastic degrading systems with this comprehensive guide from Springer. This book provides a systematic and detailed description of data-driven remaining useful life prognosis theory, making it a foundational resource for professionals and researchers. It teaches you how to utilize condition monitoring data to predict remaining useful life, enabling more effective maintenance and logistics planning. Focusing on stochastic models, methods, and applications, this text offers practical insights through a wealth of degradation monitoring experiment data. Whether you are looking to improve decision-making in management or refine engineering reliability, this volume provides the technical methods and practical prognosis approaches needed to optimize system longevity and operational efficiency.

Key Features

This Springer publication provides a systematic and detailed description of basic data-driven remaining useful life prognosis theory.

Learn to utilize condition monitoring data to predict the remaining useful life of stochastic degrading systems.

Apply practical prognosis methods to help schedule maintenance and logistics plans more effectively.

The text includes a wealth of degradation monitoring experiment data to support practical learning and application.

Focus on the specific stochastic models, methods, and applications used in remaining useful life prognosis.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
09 February 2017
Listed Since
26 October 2016

Barcode

No barcode data available

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